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I would be interested in helping with development of AI education guidelines as step 2 after the archive is established.

~Cindy Foronda

c.foronda@miami.edu

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I would be interested in helping.

~ Cindy Foronda

c.foronda@miami.edu

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I could be of assistance here; I would caution us not to (necessarily) "promise" a resource hub (if it cannot be maintained). Every educator wants one recipe on how to 'do something' about AI/ML in class

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I could be of assistance here; I would caution us not to (necessarily) "promise" a resource hub (if it cannot be maintained). Every educator wants one recipe on how to 'do something' about AI/ML in class

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Framework Planning for Innovative Patient Care Delivery

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Framework Planning for Innovative Patient Care Delivery

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Needs to be developed to guide future innovation like AI.

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How to teach the foundations of AI across nursing  specter .

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How to teach the foundations of AI across nursing  specter .

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Use of AI in Nursing Care - Development of Use Cases

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Use of AI in Nursing Care - Development of Use Cases

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Framework Planning for Innovative Patient Care Delivery

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Framework Planning for Innovative Patient Care Delivery

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1. Introduction

As health care continues to evolve and become increasingly complex, nursing education must also evolve to keep pace with the changing landscape. One significant development in higher education has been the integration of artificial intelligence (AI) technology, which has the potential to transform education by providing more personalized and efficient learning experiences for students [ 1]. As with any new technology, concerns and controversies surround the adoption of AI into higher education, including nursing programs [ 2]. This article aims to provide an overview of the state of AI in nursing education by examining its historical roots, current applications, and future directions. By discussing the opportunities and limitations of AI in nursing education, this article encourages nurse educators to reflect on how best to integrate AI technology into their teaching to enhance student learning and contribute to the development of competent and compassionate nurses.

2. AI's Roots, Growth, and Benefits

The integration of AI technology into higher education has a long history that dates back to the 1950s, when it emerged as a niche area of research with limited interest [ 3]. AI gained popularity in the 1970s and 1980s due to the development of modern computing technology [ 1]. In the 1960s, researchers investigated the use of computer-assisted instruction, and by the late 1960s, natural language processing had begun, which improved through self-play and was one of the first instances of a working machine learning system [ 4]. The use of computer-assisted instruction expanded in the 1970s, resulting in the creation of early computer-based teaching materials, such as multimedia learning resources, interactive simulations, and online tutorials, that demonstrated the potential of AI to improve experiences of teaching and learning [ 1]. In the 1990s, the use of AI-generated data through learning analytics and intelligent tutoring systems was introduced and demonstrated improved student performance [ 1]. These early experiments made it possible to provide personalized learning experiences and active learning [ 5]. A recent example of AI in nursing education is ChatGPT, which can generate a variety of mock simulation cases, such as patient interviews or job interviews, providing interactive learning experiences while saving educators' time. AI can also automate assessment and grading, allowing nursing faculty to focus on other aspects of teaching [ 2].

The use of AI in higher education has garnered global attention, with several countries investing in AI research and education. China has launched a national AI development plan to achieve significant progress in AI research and innovation by 2030 [ 6]. South Korea is dedicating significant resources to developing AI education and research with the goal of building human capacity and anticipating labor market shifts; to cultivate AI talent, the country is taking measures such as increasing the number of AI graduate schools and offering short-term intensive educational programs [ 7]. The European Union's Digital Education Action Plan 2021-2027 aims to enhance student learning and support teachers and administrative staff by promoting the use of AI in education [ 8]. The National Science Foundation in the United States is also investing in AI education and research, with a focus on improving equity in education through the use of AI-augmented learning for adult learners [ 9]. The investment in AI education and research by countries worldwide is aimed at developing leadership in the field and preparing students for the future workforce.

AI technology holds significant potential to create more sophisticated and complex simulations that can help nursing students develop critical thinking skills and prepare for real-world patient care situations. Such simulations can provide students with realistic scenarios that mimic patient care situations, allowing them to practice their clinical skills and decision making in a safe environment. As AI technology advances, these simulations will become even more advanced and sophisticated, offering an increasingly realistic and immersive learning experience. The benefits of AI in nursing education, such as interactive learning experiences and time-saving opportunities, are undeniable, but potential risks necessitate a cautious and informed approach to its use. For instance, the use of ChatGPT, an AI chatbot system, in nursing education has elicited concerns about breaches of academic integrity and ethics and theft of intellectual property [ 2]. When implemented according to proper guidelines and used ethically, however, AI can significantly improve learning experiences for nursing students and better prepare them for the challenges of a rapidly changing health care landscape.

3. Navigating the Opportunities and Challenges of AI in Nursing Education

The integration of AI into nursing education presents a wide range of opportunities, including enhanced learning outcomes and improved efficiency; however, it also poses several challenges related to privacy and security, ethical considerations, and resistance to adoption. A primary challenge associated with AI integration is the protection of student privacy. Given that AI requires access to personal information, it is essential to maintain student confidentiality and safeguard against any potential data breaches that could compromise student privacy. Ethical issues related to data bias, the exacerbation of existing inequalities, and adherence to professional standards must be thoughtfully considered and thoroughly addressed. To address such concerns, it is essential to promote ethical AI practices, provide appropriate training and support to educators and students, and implement robust privacy and security measures.

As with any new technology, resistance to the adoption of AI-based tools and techniques is a challenge that nursing education programs will likely face. Faculty members may have concerns about the potential impact of AI on their workload or their role in the teaching process; some may fear that they will need to expend a significant amount of time learning how to use AI tools or that AI could replace human educators altogether. In addressing these concerns, it is important to note that AI technology supplements and enhances the teaching and learning process when used correctly. AI-based tools can help to streamline administrative tasks and provide personalized learning experiences that cater to the unique needs of individual students. By automating routine tasks, such as grading, attendance monitoring, and student progress tracking, nurse educators gain time to focus their efforts on more complex teaching tasks that require their unique insight and expertise.

Although AI-based tools and techniques can offer significant benefits to nursing education, it is crucial to ensure that they supplement and enhance, rather than replace, human interaction, critical thinking, and creativity. The role of nurse educators in fostering these essential skills is critical to the development of competent and compassionate nurses. By striking a balance between AI-powered tools and human interaction, nursing schools can provide a more holistic and effective learning experience for students. Collaborative efforts between AI researchers and nurse educators can lead to a comprehensive and nuanced approach to nursing education that leverages the benefits of technology while preserving the value of human interaction.

To address concerns about workload and the impact of AI on their teaching role, nurse educators must receive proper training and support. Training should be designed specifically to help faculty understand the capabilities and limitations of AI technology and how it can be used to enhance the teaching and learning process. Nurse educators should be provided with guidance on how to use AI-based tools effectively and integrate them into existing teaching practices to make the most of their potential. Moreover, nurse educators can play a critical role in developing and designing AI-powered tools that align with the goals of nursing education and the core values of the nursing profession. Nurse educators can provide valuable input on the features and functionalities they consider most important, and they can ensure that tools are designed to support clinical judgment and patient-centered care.

Implementing AI-based tools in the nursing curriculum can involve significant costs and may require investments in technology and infrastructure to support their integration, such as expenses related to software, hardware, and staff training. However, by providing proper training and technical support, while involving instructional design experts and nurse educators in the development of a technology-enhanced curriculum, nursing education programs can effectively address concerns and ensure that AI is integrated responsibly and ethically in accordance with the core values of nursing education. One way to do this is by reallocating resources from existing budgets, while seeking external support for grants or collaborating with industry partners.

Investing in AI-based tools and techniques can yield long-term benefits for nursing education, such as improved learning outcomes, enhanced efficiency, increased effectiveness of teaching practices [ 5]. These benefits ultimately produce competent and compassionate nurses who are well equipped to provide high-quality care in a rapidly evolving health care landscape. Collaboration between instructional design experts, nurse educators, and AI researchers can lead to a comprehensive and nuanced approach to nursing education that leverages the benefits of technology while preserving the value of human interaction. Securing adequate funding is crucial for nursing education programs to remain at the forefront of innovation and to provide nursing students with access to some of the most up-to-date and effective learning experiences possible.

4. Conclusions

The integration of AI technology in nursing education has the potential to revolutionize by providing personalized learning experiences and improving efficiency and outcomes. However, the ethical and responsible use of AI must be ensured through careful consideration and effective strategies that address concerns such as privacy, security, bias, and adherence to professional standards. Furthermore, continued research and innovation in the field of AI in nursing education will be crucial to exploring the best practices for incorporating AI technology, examining its impact on student learning and program outcomes, and addressing ethical and legal concerns. With proper implementation and guidelines, AI tools can complement and enhance human interactions in nursing education, preparing nursing students for a rapidly changing health care landscape and advancing the nursing profession.

Acknowledgments

The author thanks Donnalee Frega for editorial assistance.

Conflicts of Interest

The author declares no conflict of interest.

Footnotes

Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

References

1. Bozkurt A., Karadeniz A., Baneres D., Guerrero-Roldán A.E., Rodríguez M.E. Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century. Sustainability. 2021;13:800. doi: 10.3390/su13020800. [[ CrossRef] Google Scholar]

2. Sun G.H., Hoelscher S.H. The ChatGPT Storm and What Faculty Can Do. Nurse Educ. 2023. Publish Ahead of Print . [ CrossRef]

4. De Bruyckere P., Kirschner P.A. Computer-Assisted Learning. In: Tatnall A., editor. Encyclopedia of Education and Information Technologies. Springer International Publishing; Cham, Switzerland: 2020. pp. 348-355. [[ CrossRef] Google Scholar]

5. Ouyang F., Zheng L., Jiao P. Artificial Intelligence in Online Higher Education: A Systematic Review of Empirical Research from 2011 to 2020. Educ. Inf. Technol. 2022;27:7893-7925. doi: 10.1007/s10639-022-10925-9. [[ CrossRef] Google Scholar]

6. Roberts H., Cowls J., Morley J., Taddeo M., Wang V., Floridi L. The Chinese Approach to Artificial Intelligence: An Analysis of Policy, Ethics, and Regulation. AI Soc. 2021;36:59-77. doi: 10.1007/s00146-020-00992-2. [[ CrossRef] Google Scholar]

Articles from International Journal of Environmental Research and Public Health are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

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The State of Artificial Intelligence in Nursing Education: Past, Present, and Future Directions

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As health care continues to evolve and become increasingly complex, nursing education must also evolve to keep pace with the changing landscape. One significant development in higher education has been the integration of artificial intelligence (AI) technology, which has the potential to transform education by providing more personalized and efficient learning experiences for students [ 1].

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Add your name if you're interested in contributing

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Tag yours or others you're aware of in the comments.

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Add your name if you're interested in contributing

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Tag yours or others you're aware of that are already doing this or have already done this (in the comments)

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Add your name if you're interested in contributing

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Add your name if you're interested in contributing

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Tag yours or others you're aware of in the comments

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Tag yours or others you're aware of in the comments.

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Dan Roberts: Including what will be examined by accreditation experts. While not specific to include “Ai”, per se, inclusion of the Domain, Contextual Statement, and Competencies, 8.1-8.5, into this book of work would be important as a measure of academic programs for entry-level and advanced practice curriculum [the file hyperlinks from the table of contents, select Domain 8… although I found the full essentials document interesting, knowing there are changes afoot].

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Artificial intelligence in nursing education: Embrace, ignore, or proceed with caution

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There are many exciting possibilities for using AI in nursing education. However, there are notable concerns about AI too. A recent editorial written by Dr. Rae Walker provides a fascinating look at the myths and harms of AI (Walker, 2022). While AI is expected to develop rapidly, it is not inevitable.

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Civics of Technology Project (Open Access Curriculum)

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Civics of Technology Project (Open Access Curriculum)

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With Dr. Ruha Benjamin- aims to empower students and educators to critically inquire into the effects of technologies on their individual and collective lives

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The Technology Reset Simulation transports students to the year 2050 where human problems related to weather disasters racism, inequality, oppression, and misinformation have only intensified. The alien Technoids arrive on Planet Earth to point out that a common villain across these problems is technology. A special task force must recommend changes to give humanity a new start.

Making technoethical decisions about edtech can be challenging. This activity draws on four different approaches to ask critical questions to avoid harm to students in our classrooms or our larger communities. The four approaches include (a) conducting a technoethical audit, (b) conducting a discriminatory design audit, (c) asking our 5 critical questions about technology, or (d) applying the Baldwin Test.

This Inquiry Design Model lesson highlights the story of the Luddites and addresses the compelling question, should we be more like the Luddites? The supporting questions ask students: Were the Luddites right to protest as they did? How should workers and citizens protest exploitative technologies today?

This Unit Guide was created to teach middle grade students about Ruth Schwartz Cowen's ground-breaking book, More Work for Mother. By reading and collecting data in their own homes, students are encouraged to answer the question, how has household labor and technology changed or stayed the same since the publication of the book in 1983? It includes modified chapters for students to read and activities for them to complete.

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Our 5 Critical Questions about Technology and Inquiry Design Model Lessons can be used to critically inquire into the collateral, unintended, & disproportionate effects of technology on our lives.

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The .gov means it's official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

doi: 10.1038/s42256-023-00651-3.

Affiliations

¹ Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.

² Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

³ Center for Applied Transgender Studies, Chicago, Illinois, United States of America.

⁴ Harvard Medical School, Boston, Massachusetts, United States of America.

Free PMC article

Free PMC article

doi: 10.1038/s42256-023-00651-3.

Affiliations

¹ Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.

² Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

³ Center for Applied Transgender Studies, Chicago, Illinois, United States of America.

⁴ Harvard Medical School, Boston, Massachusetts, United States of America.

Abstract

Fairness approaches in machine learning should involve more than assessment of performance metrics across groups. Shifting the focus away from model metrics, we reframe fairness through the lens of intersectionality, a Black feminist theoretical framework that contextualizes individuals in interacting systems of power and oppression.

Conflict of interest statement

COMPETING INTERESTS The authors declare no competing financial or non-financial interests.

Cited by

Differential Fairness: An Intersectional Framework for Fair AI.

Islam R, Keya KN, Pan S, Sarwate AD, Foulds JR. Islam R, et al. Entropy (Basel). 2023 Apr 14;25(4):660. doi: 10.3390/e25040660. Entropy (Basel). 2023. PMID: 37190449 Free PMC article.

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national center for biotechnology information

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Translating Intersectionality to Fair Machine Learning in Health Sciences - PubMed

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Fairness approaches in machine learning should involve more than assessment of performance metrics across groups. Shifting the focus away from model metrics, we reframe fairness through the lens of intersectionality, a Black feminist theoretical framework that contextualizes individuals in interacti ...

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"Decisions-embedded-in-design have significant ramifications."

Big decisions about people's lives are increasingly made by software systems and algorithms. Sorting résumés for job applications, allocating social services, and deciding who sees advertisements for open positions, housing, and products are just a few of the ways in which these software systems shape our lives.

While algorithmic decision-making can offer benefits in terms of speed, efficiency, and even fairness, bias is routinely introduced into software systems in many ways, including the use of biased training data.

Often "black boxes" with little transparency or accountability, algorithms can unfairly limit opportunities, restrict services, and produce "technological redlining" -a form of digital data discrimination that uses digital identities and activities to bolster inequality and oppression.

Algorithmic Accountability: A Primer explores issues of algorithmic accountability, or the process of assigning responsibility for harm when algorithmic decision-making results in discriminatory and inequitable outcomes.

Currently, there are few consumer or civil rights protections that limit the types of data used to build data profiles or that require the auditing of algorithmic decision-making, even though algorithmic systems can make decisions on the basis of protected attributes like race, income,or gender-even when those attributes are not referenced explicitly -because there are many effective proxies for the same information.

This brief explores the trade-offs between and debates about algorithms and accountability across several key ethical dimensions, including:

Fairness and bias;

Opacity and transparency;

The repurposing of data and algorithms;

Lack of standards for auditing;

Power and control; and

Trust and expertise.

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This primer explores issues of algorithmic accountability, or the process of assigning responsibility for harm when algorithmic decision-making results in discriminatory and inequitable outcomes.

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White House Blueprint for an A.I. Bill of Rights

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White House Blueprint for an A.I. Bill of Rights

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Among the great challenges posed to democracy today is the use of technology, data, and automated systems in ways that threaten the rights of the American public. Too often, these tools are used to limit our opportunities and prevent our access to critical resources or services. These problems are well documented. In America and around the world, systems supposed to help with patient care have proven unsafe, ineffective, or biased. Algorithms used in hiring and credit decisions have been found to reflect and reproduce existing unwanted inequities or embed new harmful bias and discrimination. Unchecked social media data collection has been used to threaten people's opportunities, undermine their privacy, or pervasively track their activity-often without their knowledge or consent.

These outcomes are deeply harmful-but they are not inevitable. Automated systems have brought about extraordinary benefits, from technology that helps farmers grow food more efficiently and computers that predict storm paths, to algorithms that can identify diseases in patients. These tools now drive important decisions across sectors, while data is helping to revolutionize global industries. Fueled by the power of American innovation, these tools hold the potential to redefine every part of our society and make life better for everyone.

This important progress must not come at the price of civil rights or democratic values, foundational American principles that President Biden has affirmed as a cornerstone of his Administration. On his first day in office, the President ordered the full Federal government to work to root out inequity, embed fairness in decision-making processes, and affirmatively advance civil rights, equal opportunity, and racial justice in America.[i] The President has spoken forcefully about the urgent challenges posed to democracy today and has regularly called on people of conscience to act to preserve civil rights-including the right to privacy, which he has called "the basis for so many more rights that we have come to take for granted that are ingrained in the fabric of this country."[ii]

To advance President Biden's vision, the White House Office of Science and Technology Policy has identified five principles that should guide the design, use, and deployment of automated systems to protect the American public in the age of artificial intelligence. The Blueprint for an AI Bill of Rights is a guide for a society that protects all people from these threats-and uses technologies in ways that reinforce our highest values. Responding to the experiences of the American public, and informed by insights from researchers, technologists, advocates, journalists, and policymakers, this framework is accompanied by From Principles to Practice-a handbook for anyone seeking to incorporate these protections into policy and practice, including detailed steps toward actualizing these principles in the technological design process. These principles help provide guidance whenever automated systems can meaningfully impact the public's rights, opportunities, or access to critical needs.

You should be protected from unsafe or ineffective systems. Automated systems should be developed with consultation from diverse communities, stakeholders, and domain experts to identify concerns, risks, and potential impacts of the system. Systems should undergo pre-deployment testing, risk identification and mitigation, and ongoing monitoring that demonstrate they are safe and effective based on their intended use, mitigation of unsafe outcomes including those beyond the intended use, and adherence to domain-specific standards. Outcomes of these protective measures should include the possibility of not deploying the system or removing a system from use. Automated systems should not be designed with an intent or reasonably foreseeable possibility of endangering your safety or the safety of your community. They should be designed to proactively protect you from harms stemming from unintended, yet foreseeable, uses or impacts of automated systems. You should be protected from inappropriate or irrelevant data use in the design, development, and deployment of automated systems, and from the compounded harm of its reuse. Independent evaluation and reporting that confirms that the system is safe and effective, including reporting of steps taken to mitigate potential harms, should be performed and the results made public whenever possible.

From Principles to Practice: Safe and Effective Systems

You should not face discrimination by algorithms and systems should be used and designed in an equitable way. Algorithmic discrimination occurs when automated systems contribute to unjustified different treatment or impacts disfavoring people based on their race, color, ethnicity, sex (including pregnancy, childbirth, and related medical conditions, gender identity, intersex status, and sexual orientation), religion, age, national origin, disability, veteran status, genetic information, or any other classification protected by law. Depending on the specific circumstances, such algorithmic discrimination may violate legal protections. Designers, developers, and deployers of automated systems should take proactive and continuous measures to protect individuals and communities from algorithmic discrimination and to use and design systems in an equitable way. This protection should include proactive equity assessments as part of the system design, use of representative data and protection against proxies for demographic features, ensuring accessibility for people with disabilities in design and development, pre-deployment and ongoing disparity testing and mitigation, and clear organizational oversight. Independent evaluation and plain language reporting in the form of an algorithmic impact assessment, including disparity testing results and mitigation information, should be performed and made public whenever possible to confirm these protections.

From Principles to Practice: Algorithmic Discrimination Protections

You should be protected from abusive data practices via built-in protections and you should have agency over how data about you is used. You should be protected from violations of privacy through design choices that ensure such protections are included by default, including ensuring that data collection conforms to reasonable expectations and that only data strictly necessary for the specific context is collected. Designers, developers, and deployers of automated systems should seek your permission and respect your decisions regarding collection, use, access, transfer, and deletion of your data in appropriate ways and to the greatest extent possible; where not possible, alternative privacy by design safeguards should be used. Systems should not employ user experience and design decisions that obfuscate user choice or burden users with defaults that are privacy invasive. Consent should only be used to justify collection of data in cases where it can be appropriately and meaningfully given. Any consent requests should be brief, be understandable in plain language, and give you agency over data collection and the specific context of use; current hard-to-understand notice-and-choice practices for broad uses of data should be changed. Enhanced protections and restrictions for data and inferences related to sensitive domains, including health, work, education, criminal justice, and finance, and for data pertaining to youth should put you first. In sensitive domains, your data and related inferences should only be used for necessary functions, and you should be protected by ethical review and use prohibitions. You and your communities should be free from unchecked surveillance; surveillance technologies should be subject to heightened oversight that includes at least pre-deployment assessment of their potential harms and scope limits to protect privacy and civil liberties. Continuous surveillance and monitoring should not be used in education, work, housing, or in other contexts where the use of such surveillance technologies is likely to limit rights, opportunities, or access. Whenever possible, you should have access to reporting that confirms your data decisions have been respected and provides an assessment of the potential impact of surveillance technologies on your rights, opportunities, or access.

From Principles to Practice: Data Privacy

You should know that an automated system is being used and understand how and why it contributes to outcomes that impact you. Designers, developers, and deployers of automated systems should provide generally accessible plain language documentation including clear descriptions of the overall system functioning and the role automation plays, notice that such systems are in use, the individual or organization responsible for the system, and explanations of outcomes that are clear, timely, and accessible. Such notice should be kept up-to-date and people impacted by the system should be notified of significant use case or key functionality changes. You should know how and why an outcome impacting you was determined by an automated system, including when the automated system is not the sole input determining the outcome. Automated systems should provide explanations that are technically valid, meaningful and useful to you and to any operators or others who need to understand the system, and calibrated to the level of risk based on the context. Reporting that includes summary information about these automated systems in plain language and assessments of the clarity and quality of the notice and explanations should be made public whenever possible.

From Principles to Practice: Notice and Explanation

You should be able to opt out, where appropriate, and have access to a person who can quickly consider and remedy problems you encounter. You should be able to opt out from automated systems in favor of a human alternative, where appropriate. Appropriateness should be determined based on reasonable expectations in a given context and with a focus on ensuring broad accessibility and protecting the public from especially harmful impacts. In some cases, a human or other alternative may be required by law. You should have access to timely human consideration and remedy by a fallback and escalation process if an automated system fails, it produces an error, or you would like to appeal or contest its impacts on you. Human consideration and fallback should be accessible, equitable, effective, maintained, accompanied by appropriate operator training, and should not impose an unreasonable burden on the public. Automated systems with an intended use within sensitive domains, including, but not limited to, criminal justice, employment, education, and health, should additionally be tailored to the purpose, provide meaningful access for oversight, include training for any people interacting with the system, and incorporate human consideration for adverse or high-risk decisions. Reporting that includes a description of these human governance processes and assessment of their timeliness, accessibility, outcomes, and effectiveness should be made public whenever possible.

From Principles to Practice: Human Alternatives, Consideration, and Fallback

Applying the Blueprint for an AI Bill of Rights

While many of the concerns addressed in this framework derive from the use of AI, the technical capabilities and specific definitions of such systems change with the speed of innovation, and the potential harms of their use occur even with less technologically sophisticated tools.

Thus, this framework uses a two-part test to determine what systems are in scope. This framework applies to (1) automated systems that (2) have the potential to meaningfully impact the American public's rights, opportunities, or access to critical resources or services. These Rights, opportunities, and access to critical resources of services should be enjoyed equally and be fully protected, regardless of the changing role that automated systems may play in our lives.
This framework describes protections that should be applied with respect to all automated systems that have the potential to meaningfully impact individuals' or communities' exercise of: Rights, Opportunities, or Access
Civil rights, civil liberties, and privacy, including freedom of speech, voting, and protections from discrimination, excessive punishment, unlawful surveillance, and violations of privacy and other freedoms in both public and private sector contexts;

Equal opportunities, including equitable access to education, housing, credit, employment, and other programs; or,

Access to critical resources or services, such as healthcare, financial services, safety, social services, non-deceptive information about goods and services, and government benefits.

A list of examples of automated systems for which these principles should be considered is provided in the Appendix. The Technical Companion, which follows, offers supportive guidance for any person or entity that creates, deploys, or oversees automated systems.

Considered together, the five principles and associated practices of the Blueprint for an AI Bill of Rights form an overlapping set of backstops against potential harms. This purposefully overlapping framework, when taken as a whole, forms a blueprint to help protect the public from harm. The measures taken to realize the vision set forward in this framework should be proportionate with the extent and nature of the harm, or risk of harm, to people's rights, opportunities, and access.

[i] The Executive Order On Advancing Racial Equity and Support for Underserved Communities Through the Federal Government. https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/20/executive-order-advancing-racial-equity-and-support-for-underserved-communities-through-the-federal-government/

[ii] The White House. Remarks by President Biden on the Supreme Court Decision to Overturn Roe v. Wade. Jun. 24, 2022. https://www.whitehouse.gov/briefing-room/speeches-remarks/2022/06/24/remarks-by-president-biden-on-the-supreme-court-decision-to-overturn-roe-v-wade/

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Blueprint for an AI Bill of Rights | OSTP | The White House

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Among the great challenges posed to democracy today is the use of technology, data, and automated systems in ways that threaten the rights of the American public. Too often, these tools are used to limit our opportunities and prevent our access to critical resources or services. These problems are well documented.

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Consentful Tech Curriculum (Open Access)

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Consentful Tech Curriculum (Open Access)

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The Consentful Tech Project – The Consentful Tech Project raises awareness, develops strategies, and shares skills to help people build and use technology consentfully.

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An interdisciplinary tech ethics & accountable design think tank at UMass Amherst. Includes nursing, computer science, public health, public policy, social work, exercise science, fine arts, & gender studies. Developing guidelines & best practices for ethical design and use of tech in health care. Part of IALS Center for Personalized Health Monitoring

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Our Data Bodies Project - Digital Defense Workbook (Open Access incl Spanish)

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Our Data Bodies Project - Digital Defense Workbook (Open Access incl Spanish)

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The Our Data Bodies (ODB) Project is a collaborative, participatory research and organizing effort working in three cities: Charlotte, North Carolina; Detroit, Michigan; and Los Angeles California. This report details our preliminary findings from our first year. Download our first community report.

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The Our Data Bodies (ODB) Project is a collaborative, participatory research and organizing effort working in three cities: Charlotte, North Carolina; Detroit, Michigan; and Los Angeles California. This report details our preliminary findings from our first year. Download our first community report.

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Additional guidance on curricula that must go beyond informatics/computer science to fully incorporate ethics/humanities/accountability

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Additional guidance on curricula that must go beyond informatics/computer science to fully incorporate ethics/humanities/accountability

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Algorithmic bias in artificial intelligence is a problem-And the root issue is power - PubMed

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Its neither feasible nor just to expect individual nurses to counter systemic injustice in health care through individual actions, more technocentric curricula, or industry partnerships. We need disciplinary supports for collective action to renegotiate power for AI tech.

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Obermeyer et al. - Dissecting racial bias in algorithm used to manage health of populations

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Obermeyer et al. - Dissecting racial bias in algorithm used to manage health of populations

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From founder of the Algorithmic Justice League Joy Buolamwini & colleagues

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Coded Bias | A.I. Bias & Facial Recognition Discrimination | Documentary | PBS

Premiered March 22, 2021

Directed by

Shalini Kantayya

Coded Bias exposes prejudices and threats to civil liberty in facial recognition algorithms and artificial intelligence.

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About the Documentary

In an increasingly data-driven, automated world, the question of how to protect individuals' civil liberties in the face of artificial intelligence looms larger by the day. Coded Bias follows M.I.T. Media Lab computer scientist Joy Buolamwini, along with data scientists, mathematicians, and watchdog groups from all over the world, as they fight to expose the discrimination within algorithms now prevalent across all spheres of daily life.

While conducting research on facial recognition technologies at the M.I.T. Media Lab, Buolamwini, a "poet of code," made the startling discovery that some algorithms could not detect dark-skinned faces or classify women with accuracy. This led to the harrowing realization that the very machine-learning algorithms intended to avoid prejudice are only as unbiased as the humans and historical data programming them.

Coded Bias documents the dramatic journey that follows, from discovery to exposure to activism, as Buolamwini goes public with her findings and undertakes an effort to create a movement toward accountability and transparency, including testifying before Congress to push for the first-ever legislation governing facial recognition in the United States and starting the Algorithmic Justice League. The film also includes data journalist Meredith Broussard; Silkie Carlo, director of Big Brother Watch, who is monitoring the trial use of facial recognition technology by U.K. police; Virginia Eubanks, author of Automating Inequality ; Ravi Naik, human rights lawyer and media commentator; Dr. Safiya Umoja Noble, author and expert on algorithmic discrimination and technology bias; and Zeynep Tufekci, author of Twitter and Teargas .

The Filmmaker

Shalini Kantayya's Coded Bias premiered at Sundance, was Emmy-nominated for Outstanding Science and Technology Documentary and a Critics' Choice Award. Her debut feature, Catching the Sun, was released on Netflix and named a NY Times Critic's Pick. Her recent film TikTok, Boom. was nominated for the Grand Jury Prize at 2022 Sundance Film.

Full Credits

Awards

Sundance Film Festival

Grand Jury Prize (Nominee)

Hamptons International Film Festival

Excellence in Documentary Filmmaking

Calgary International Film Festival

Best International Documentary

Learn more about the documentary

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Explores racial prejudices that exist in facial recognition algorithms, and the threat artificial intelligence poses to civil liberties

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An exploration of how design might be led by marginalized communities, dismantle structural inequality, and advance collective liberation and ecological survival.
What is the relationship between design, power, and social justice? "Design justice" is an approach to design that is led by marginalized communities and that aims explicitly to challenge, rather than reproduce, structural inequalities. It has emerged from a growing community of designers in various fields who work closely with social movements and community-based organizations around the world.

This book explores the theory and practice of design justice, demonstrates how universalist design principles and practices erase certain groups of people -specifically, those who are intersectionally disadvantaged or multiply-burdened under the matrix of domination (white supremacist heteropatriarchy, ableism, capitalism, and settler colonialism) - and invites readers to "build a better world, a world where many worlds fit; linked worlds of collective liberation and ecological sustainability." Along the way, the book documents a multitude of real-world community-led design practices, each grounded in a particular social movement. Design Justice goes beyond recent calls for design for good, user-centered design, and employment diversity in the technology and design professions; it connects design to larger struggles for collective liberation and ecological survival.

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An exploration of how design might be led by marginalized communities, dismantle structural inequality, and advance collective liberation and ecological survival.

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AMIA Nursing Informatics Working Group presentation - On Health Equity & Accountability in an A.I. Era

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AMIA Nursing Informatics Working Group presentation - On Health Equity & Accountability in an A.I. Era